Keywords
Artificial Intelligence, Generative AI, Emerging Technologies, Digital Transformation, Higher Education
Abstract
The rapid development of generative artificial intelligence (GenAI) tools, such as ChatGPT, is having a significant impact on higher education in in Ibero-America—a region marked by profound structural inequalities—remains critically understudied. This study uses a sequential explanatory research method to contribute to Sustainable Development Goal (SDG) 4, “Quality Education,” for equitable, inclusive quality education, using mixed methods to investigate the adoption of GenAI in 17 countries. Data was collected using a structured electronic questionnaire, which was completed by 1,523 university students. Thirty-two semi-structured interviews were also conducted with professors from private and public universities. This expansive sample is essential to capture the region’s vast socio-economic, cultural, and digital diversity, ensuring findings are representative and robust beyond isolated contexts. The PLS-SEM Comunicar, 85, XXXIV, 2026 research identified that trust (? = 0.538) and ease of use perception (? = 0.475) have a significant influence on the intention to remain (? = 0.859) and effective use. Although 66% of the teachers interviewed expressed satisfaction with GenAI, highlighting its effect in saving time and encouraging research, there are still priority concerns. Challenges include digital inequalities: 47% of those interviewed mentioned poor infrastructure and 44% pointed to a digital divide affecting rural or underserved regions. More than 40% of participants pointed to ethical issues, such as misinformation, plagiarism, and privacy risks. However, the study also identifies unique latent opportunities—where GenAI could potentially ‘leapfrog’ traditional barriers to enable personalized learning in under-resourced classrooms and foster pedagogical innovation. The findings underscore that a one-size-fits-all approach is ineffective. The study concludes with concrete recommendations for policymakers and educators, advocating for the development of context-specific ethical frameworks, strategic investment in inclusive digital infrastructure, and the creation of pedagogic models tailored to Ibero-America’s unique challenges and opportunities.
References
Abreu, A., Rocha, Á. y Cota, M. P. (2015). Caderneta Eletrónica no Processo Ensino-Aprendizagem: Visão de Professores e Pais de alunos do ensino Básico e Secundário. RISTI-Revista Ibérica de Sistemas e Tecnologias de Informação, (16), 108-128. https://doi.org/10.17013/RISTI.16.108-128
Agüero-Torales, M. M., López-Herrera, A. G. y Vilares, D. (2023). Multidimensional Affective Analysis for Low-Resource Languages: A Use Case with Guarani-Spanish Code-Switching Language. Cognitive Computation, 15(4), 1391-1406. https://doi.org/10.1007/s12559-023-10165-0
Andocilla-Oleas, I., Mayorga-Jácome, T. y Perez-Cargua, M. (2025). Analysis of Literacy in Artificial Intelligence in Education: An Approach from Teaching. En A. Abreu, J. V. Carvalho, A. Mesquita, A. Sousa Pinto, y M. Mendonça Teixeira (Eds.), Perspectives and Trends in Education and Technology (pp. 145-153). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-78155-1_14
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Boomsma, A. y Hoogland, J. J. (2001). The Robustness of LISREL Modeling Revisited. En R. Cudeck, S. du Toit, y D. Sörbom (Eds.), Structural Equation Models: Present and Future. A Festschrift in Honor of Karl Jöreskog (pp. 139-168). Scientific Software International. https://api.semanticscholar.org/CorpusID:17339260
Cambra-Fierro, J. J., Blasco, M. F., López-Pérez, M.-E. E. y Trifu, A. (2024). ChatGPT adoption and its influence on faculty well-being: An empirical research in higher education. Education and Information Technologies, 30(2), 1517-1538. https://doi.org/10.1007/s10639-024-12871-0
Carvalho, P. E. A. (2023). Explorando os potenciais e enfrentando os desafios para o uso do chatgpt em sala de aula: Desafios históricos e pedagógicos. Convergências: estudos em Humanidades Digitais, 1(2), 210-226. https://doi.org/10.59616/cehd.v1i2.246
Coutinho, C. P. (2014). Metodologia de investigação em ciências sociais e humanas. Leya.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Essenfelder, R. (2024). Boas notícias, más opiniões: a cobertura de imprensa sobre o ChatGPT em Portugal. Comunicação & Inovação, 25, e20249691. https://www.seer.uscs.edu.br/index.php/revista_comunicacao_inovacao/article/view/9691
Ferreira, H. M., Pinto, A. S., da Costa Leonídio, U. y Afonso, G. (2023). Interpreting the Unified Theory of Acceptance and Use of Technology from the Perspective of Teachers of Higher Education Institutions in Brazil. En A. Mesquita, A. Abreu, J. V. Carvalho, y C. H. P. de Mello (Eds.), Perspectives and Trends in Education and Technology (pp. 843-855). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6585-2_75
Fornell, C. y Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Galdames, I. S. (2025). Impact of Artificial Intelligence on Higher Education: A Literature Review. En A. Abreu, J. V. Carvalho, A. Mesquita, A. Sousa Pinto, y M. Mendonça Teixeira (Eds.), Perspectives and Trends in Education and Technology (pp. 373-392). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-78155-1_35
Guest, G., Bunce, A. y Johnson, L. (2006). How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability. Field Methods, 18(1), 59-82. https://doi.org/10.1177/1525822X05279903
Hair, J., Hult, G. T. M., Ringle, C. y Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS SEM) (2.a ed.). Sage.
Hu, L.-t. y Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453. https://doi.org/10.1037/1082-989X.3.4.424
Keith, L. R. y Waldron, L. (2024). AI-Driven Educational Paradigm for Sustainable Development Goal 4: A Caribbean Perspective. The UWI Quality Education Forum, (28), 151-171. https://journals.sta.uwi.edu/ojs/index.php/qef/article/view/9343
Latam-GPT. (2025). Latam-GPT. https://www.latamgpt.org/en
Lohmöller, J.-B. (1989). Basic Principles of Model Building. En J.-B. Lohmöller (Ed.), Latent Variable Path Modeling with Partial Least Squares (pp. 13-26). Physica-Verlag HD. https://doi.org/10.1007/978-3-642-52512-4_1
Madden, O., Paiva, J., Prince, A., Higgs, R. y Stennett, K. (2025). IA Generativa e o Futuro Papel e Profissionalizaçao dos Professores de L?nguas Estrangeiras: Perspetivas das Cara?bas. https://www.researchgate.net/profile/Jeronimo-Paiva/publication/398860298
Maritaca. (2025). Maritaca AI: IA Brasileira com Foco em LLM para Português | Maritalk. https://www.maritaca.ai
Mishra, A., Shukla, A., Rana, N. P., Currie, W. L. y Dwivedi, Y. K. (2023). Re-examining post-acceptance model of information systems continuance: A revised theoretical model using MASEM approach. International Journal of Information Management, 68, 102571. https://doi.org/10.1016/j.ijinfomgt.2022.102571
Netemeyer, R. G., Bearden, W. O. y Sharma, S. (2003). Scaling Procedures: Issues and Applications. Sage Publications. https://doi.org/10.4135/9781412985772
Oliveira, M. M., Santos, L. D. y Fortuna, N. (2011). Econometria. Escolar Editora. https://escolareditora.com/livro/128696
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.1177/002224378001700405
Ookla. (2025). Speedtest Global Index – Internet Speed around the world. https://www.speedtest.net/global-index
Parreira, P., Proença, S., Sousa, L. B. y Mónico, L. (2018). Technology Acceptance Model (TAM): Modelos percursores e modelos evolutivos. En P. Parreira, L. Alves, L. Mónico, J. H. Sampaio, y T. Paiva (Eds.), Competências empreendedoras no Ensino Superior Politécnico: Motivos, influências, serviços de apoio e educação (pp. 143-166). Instituto Politécnico da Guarda. https://www.researchgate.net/publication/327285963
Pestana, M. H. y Gageiro, J. N. (2014). Análise de dados para ciências sociais: a complementaridade do SPSS 6 ª Edição Revista, Atualizada E Aumentada Maria Helena Pestana João Nunes Gageiro. https://doi.org/10.13140/2.1.2491.7284
Pinto, A. S., Abreu, A., Carvalho, J. V., Carvalho, M., Martins, S. y Paiva, J. (2025). The Technology Acceptance Model (TAM): A Meta-Analytic Structural Equation Modeling (MASEM) Approach to Explaining the Adoption of GenAI Tools in Higher Education. En J. V. Carvalho, A. Abreu, M. Silva, E. Ferreira da Costa, y J. A. F. Costa (Eds.), Perspectives and Trends in Education and Technology, Volume 2 (pp. 470-480). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-09080-5_46
Pinto, A. S., Abreu, A., Costa, E. y Paiva, J. (2023). How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review. Journal of Information Systems Engineering and Management, 8(2), 21168. https://doi.org/10.55267/iadt.07.13227
Pinto, A. S., Abreu, A., Costa, E. y Paiva, J. (2024). AI in Accounting: Can AI Models Like ChatGPT and Gemini Successfully Pass the Portuguese Chartered Accountant Exam? En A. Abreu, J. V. Carvalho, A. Mesquita, A. Sousa Pinto, y M. Mendonça Teixeira (Eds.), Perspectives and Trends in Education and Technology (pp. 429-438). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-78155-1_40
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y. y Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Ringle, C. M., Da Silva, D. y de Souza Bido, D. (2014). Modelagem de equações estruturais com utilização do SmartPLS. ReMark - Revista Brasileira De Marketing, 13(2), 56-73. https://doi.org/10.5585/remark.v13i2.2717
Riofrancos, T. (2020, November 11). Extractivism and Extractivismo. Global South Studies. https://www.globalsouthstudies.org/keyword-essay/extractivism-and-extractivismo
Saúde, S., Barros, J. P. y Almeida, I. (2024). Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Social Sciences, 13(8), 410. https://doi.org/10.3390/socsci13080410
Schilling, J. (2006). On the Pragmatics of Qualitative Assessment. European Journal of Psychological Assessment, 22(1), 28-37. https://doi.org/10.1027/1015-5759.22.1.28
Singh, S. y Paiva, J. (2025). The role of AI characteristics and their influence on higher education students’ continuance intention to use GenAI tools. Information Discovery and Delivery. https://doi.org/10.1108/idd-03-2025-0060
Statista. (2018). Share of indigenous or native language speakers in Latin America in 2018. Statista. https://www.statista.com/statistics/1058273/latin-america-share-indigenous-language-speakers-country
UNESCO. (2024). Global education monitoring report, 2024/5, Leadership in education: lead for learning. United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54676/EFLH5184
Venkatesh, V., Morris, M. G., Davis, G. B. y Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. y Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
Vilma, G.-G., Oswaldo, D. W., Kenia, C.-V., Martha, C.-Q., Mónica, L.-D., Roberth, P.-M. y Daniela, L. L. (2025). Artificial Intelligence in Higher Educational Practice. Perspectives of Teacher and Students. En A. Abreu, J. V. Carvalho, A. Mesquita, A. Sousa Pinto, y M. Mendonça Teixeira (Eds.), Perspectives and Trends in Education and Technology (pp. 439-450). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-78155-1_41
webQDA. (2024). WebQDA – Qualitative Data Analysis Software. https://www.webqda.net
Fundref
Los autores desean agradecer al ISCAP-IPP por el apoyo institucional y al CEOS.PP por su respaldo a esta investigación. Este trabajo fue apoyado por la Fundação para a Ciência e a Tecnologia (FCT), Portugal, a través de la financiación del proyecto UIDB/04007/2020 (CEOS.PP).
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Received: 2025-08-07 | Reviewed: 2025-09-19 | Accepted: 2025-09-30 | Online First: 2026-04-11 | Published: 2026-04-15
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Agostinho Sousa Pinto., António Abreu., Manuel Pérez Cota., Jerónimo Paiva. (2026). Generative AI in Ibero-American Higher Education: Adoption Factors, Regional Challenges, and Opportunities for Educational Innovation. Comunicar, 34(85). 10.5281/zenodo.19690173
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